Search Results for author: Christopher Metzler

Found 8 papers, 0 papers with code

Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion

no code implementations6 Apr 2024 Ziyuan Qu, Omkar Vengurlekar, Mohamad Qadri, Kevin Zhang, Michael Kaess, Christopher Metzler, Suren Jayasuriya, Adithya Pediredla

In this manuscript, we demonstrate that using transient data (from sonars) allows us to address the missing cone problem by sampling high-frequency data along the depth axis.

Autonomous Navigation Novel View Synthesis

TimeRewind: Rewinding Time with Image-and-Events Video Diffusion

no code implementations20 Mar 2024 Jingxi Chen, Brandon Y. Feng, Haoming Cai, Mingyang Xie, Christopher Metzler, Cornelia Fermuller, Yiannis Aloimonos

Through extensive experimentation, we demonstrate the capability of our approach to synthesize high-quality videos that effectively ``rewind'' time, showcasing the potential of combining event camera technology with generative models.

A Scalable Training Strategy for Blind Multi-Distribution Noise Removal

no code implementations30 Oct 2023 Kevin Zhang, Sakshum Kulshrestha, Christopher Metzler

Our work improves upon a recently proposed universal denoiser training strategy by extending these results to higher dimensions and by incorporating a polynomial approximation of the true specification-loss landscape.

Active Learning Denoising

Snapshot High Dynamic Range Imaging with a Polarization Camera

no code implementations16 Aug 2023 Mingyang Xie, Matthew Chan, Christopher Metzler

By placing a linear polarizer in front of the polarization camera, we are able to simultaneously capture four images with varied exposures, which are determined by the orientation of the polarizer.

Seeing the World through Your Eyes

no code implementations15 Jun 2023 Hadi AlZayer, Kevin Zhang, Brandon Feng, Christopher Metzler, Jia-Bin Huang

The reflective nature of the human eye is an underappreciated source of information about what the world around us looks like.

Matching Plug-and-Play Algorithms to the Denoiser

no code implementations NeurIPS Workshop Deep_Invers 2021 Saurav K Shastri, Rizwan Ahmad, Christopher Metzler, Philip Schniter

To solve inverse problems, plug-and-play (PnP) methods have been developed that replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network (DNN).

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